Open Access
ARTICLE
Improving Performance Prediction on Education Data with Noise and Class Imbalance
Akram M. Radwana,b, Zehra Cataltepea,c
a Computer Engineering Department, Istanbul Technical University, Istanbul, Turkey;
b Department of Information Technology, University College of Applied Sciences, Gaza, Palestine;
c tazi.io Machine Learning Solutions, Istanbul, Turkey
* Corresponding Author: Akram M. Radwan,
Intelligent Automation & Soft Computing 2018, 24(4), 777-783. https://doi.org/10.1080/10798587.2017.1337673
Abstract
This paper proposes to apply machine learning techniques to predict students’ performance on two
real-world educational data-sets. The first data-set is used to predict the response of students with
autism while they learn a specific task, whereas the second one is used to predict students’ failure at a
secondary school. The two data-sets suffer from two major problems that can negatively impact the
ability of classification models to predict the correct label; class imbalance and class noise. A series
of experiments have been carried out to improve the quality of training data, and hence improve
prediction results. In this paper, we propose two noise filter methods to eliminate the noisy instances
from the majority class located inside the borderline area. Our methods combine the over-sampling
SMOTE technique with the thresholding technique to balance the training data and choose the best
boundary between classes. Then we apply a noise detection approach to identify the noisy instances.
We have used the two data-sets to assess the efficacy of class-imbalance approaches as well as both
proposed methods. Results for different classifiers show that, the AUC scores significantly improved
when the two proposed methods combined with existing class-imbalance techniques.
Keywords
Cite This Article
A. M. Radwan and Z. Cataltepe, "Improving performance prediction on education data with noise and class imbalance,"
Intelligent Automation & Soft Computing, vol. 24, no.4, pp. 777–783, 2018.